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Artificial Intelligence for Software Engineering: The Journey So Far and the Road Ahead
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Outgoing claims: the papers this work cites, builds on, or argues with.
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Generative AI for Software Practitioners
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2021 IEEE Symposium on Security and Privacy (SP)
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Generative Adversarial Networks-Based Imbalance Learning in Software Aging-Related Bug Prediction
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The Oracle Problem in Software Testing: A Survey
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Machine Learning-Based Prototyping of Graphical User Interfaces for Mobile Apps
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An Empirical Study of Model-Agnostic Techniques for Defect Prediction Models
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Machine/Deep Learning for Software Engineering: A Systematic Literature Review
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Code to comment "translation"
inferredcites →https://doi.org/10.1145/3377811openalex-snapshot
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Explainability for Large Language Models: A Survey
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Which Syntactic Capabilities Are Statistically Learned by Masked Language Models for Code?
inferredcites →https://doi.org/10.2139/ssrn.4724885openalex-snapshot
A3test: Assertion-Augmented Automated Test Case Generation
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Agile Methodology for the Standardization of Engineering Requirements Using Large Language Models
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Estimating the Carbon Footprint of BLOOM, a 176B Parameter Language Model
inferredcites →https://doi.org/10.48550/arxiv.2301.03846openalex-snapshot
Practitioners' Expectations on Code Completion
inferredcites →https://doi.org/10.48550/arxiv.2301.13246openalex-snapshot
Conversational Automated Program Repair
inferredcites →https://doi.org/10.48550/arxiv.2302.10352openalex-snapshot
A3Test: Assertion-Augmented Automated Test Case Generation
inferredcites →https://doi.org/10.48550/arxiv.2307.13339openalex-snapshot
Analyzing Chain-of-Thought Prompting in Large Language Models via Gradient-based Feature Attributions
inferredcites →https://doi.org/10.48550/arxiv.2308.10620openalex-snapshot
Large Language Models for Software Engineering: A Systematic Literature Review
inferredcites →https://doi.org/10.48550/arxiv.2310.05797openalex-snapshot
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Incoming claims: where this work appears in other papers’ reference lists and arguments.
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